scholarly journals Winterization of Texan power system infrastructure is profitable but risky

2021 ◽  
Author(s):  
Katharina Gruber ◽  
Tobias Gauster ◽  
Gregor Laaha ◽  
Peter Regner ◽  
Johannes Schmidt
Keyword(s):  
Time Series ◽  
Power Plant ◽  
Power Plants ◽  
Large Scale ◽  
Low Frequency ◽  
Cold Spell ◽  
Climate Data ◽  
Temperature Dependent ◽  
Long Time ◽  
Regulatory Measures

We deliver the first analysis of the 2021 cold spell in Texas which combines temperature dependent load estimates with temperature dependent estimates of power plant outages to understand the frequency of loss of load events, using a 71 year long time series of climate data. The expected avoided loss from full winterization is 11.74bn\$ over a 30 years investment period. We find that large-scale winterization, in particular of gas infrastructure and gas power plants, would be profitable, as related costs for winterization are substantially lower. At the same moment, the necessary investments involve risk due to the low-frequency of events – the 2021 event was the largest and we observe only 8 other similar ones in the set of 71 simulated years. Regulatory measures may therefore be necessary to enforce winterization.

scholarly journals Clustering time series applied to energy markets

2019 ◽  
Vol 2 (S1) ◽  
Author(s):  
Cornelia Krome ◽  
Jan Höft ◽  
Volker Sander
Keyword(s):  
Time Series ◽  
Power Plant ◽  
Electricity Market ◽  
Power Plants ◽  
Large Scale ◽  
Energy Systems ◽  
Weather Data ◽  
Virtual Power ◽  
Virtual Power Plant ◽  
Virtual Power Plants

Abstract In Germany and many other countries the energy market has been subject to significant changes. Instead of only a few large-scale producers that serve aggregated consumers, a shift towards regenerative energy sources is taking place. Energy systems are increasingly being made more flexible by decentralised producers and storage facilities, i.e. many consumers are also producers. The aggregation of producers form another type of power plants: a virtual power plant. On the basis of aggregated production and consumption, virtual power plants try to make decisions under the conditions of the electricity market or the grid condition. They are influenced by many different aspects. These include the current feed-in, weather data, or the demands of the consumers. Clearly, a virtual power plant is focusing on developing strategies to influence and optimise these factors. To accomplish this, many data sets can and should be analysed in order to interpret and create forecasts for energy systems. Time series based analytics are therefore of particular interest for virtual power plants. Classifying the different time series according to generators, consumers or customer types simplifies processes. In this way, scalable solutions for forecasts can be found. However, one has to first find the according clusters efficiently. This paper presents a method for determining clusters of time series. Models are adapted and model-based clustered using ARIMA parameters and an individual quality measure. In this way, the analysis of generic time series can be simplified and additional statements can be made with the help of graphical evaluations. To facilitate large scale virtual power plants, the presented clustering workflow is prepared to be applied on big data capable platforms, e.g. time series stored in Apache Cassandra, analysed through an Apache Spark execution framework. The procedure is shown here using the example of the Day-Ahead prices of the electricity market for 2018.

scholarly journals Estimating CO2 Emissions from Large Scale Coal-Fired Power Plants Using OCO-2 Observations and Emission Inventories

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 811
Author(s):  
Yaqin Hu ◽  
Yusheng Shi
Keyword(s):  
Power Plant ◽  
Co2 Emissions ◽  
Atmospheric Carbon Dioxide ◽  
Power Plants ◽  
Large Scale ◽  
Global Climate ◽  
Gaussian Model ◽  
Point Sources ◽  
Emission Inventories ◽  
Bottom Up

The concentration of atmospheric carbon dioxide (CO2) has increased rapidly worldwide, aggravating the global greenhouse effect, and coal-fired power plants are one of the biggest contributors of greenhouse gas emissions in China. However, efficient methods that can quantify CO2 emissions from individual coal-fired power plants with high accuracy are needed. In this study, we estimated the CO2 emissions of large-scale coal-fired power plants using Orbiting Carbon Observatory-2 (OCO-2) satellite data based on remote sensing inversions and bottom-up methods. First, we mapped the distribution of coal-fired power plants, displaying the total installed capacity, and identified two appropriate targets, the Waigaoqiao and Qinbei power plants in Shanghai and Henan, respectively. Then, an improved Gaussian plume model method was applied for CO2 emission estimations, with input parameters including the geographic coordinates of point sources, wind vectors from the atmospheric reanalysis of the global climate, and OCO-2 observations. The application of the Gaussian model was improved by using wind data with higher temporal and spatial resolutions, employing the physically based unit conversion method, and interpolating OCO-2 observations into different resolutions. Consequently, CO2 emissions were estimated to be 23.06 ± 2.82 (95% CI) Mt/yr using the Gaussian model and 16.28 Mt/yr using the bottom-up method for the Waigaoqiao Power Plant, and 14.58 ± 3.37 (95% CI) and 14.08 Mt/yr for the Qinbei Power Plant, respectively. These estimates were compared with three standard databases for validation: the Carbon Monitoring for Action database, the China coal-fired Power Plant Emissions Database, and the Carbon Brief database. The comparison found that previous emission inventories spanning different time frames might have overestimated the CO2 emissions of one of two Chinese power plants on the two days that the measurements were made. Our study contributes to quantifying CO2 emissions from point sources and helps in advancing satellite-based monitoring techniques of emission sources in the future; this helps in reducing errors due to human intervention in bottom-up statistical methods.

Output Power Correlation Between Nearby Wind Power Plants

2003 ◽  
Author(s):  
Yih-Huei Wan ◽  
Michael Milligan ◽  
Brian Parsons
Keyword(s):  
Power Plant ◽  
Wind Power ◽  
Power Output ◽  
High Frequency ◽  
Power Plants ◽  
Meteorological Data ◽  
Low Frequency ◽  
Spatial Separation ◽  
Wind Power Plant ◽  
Wind Power Plants

The National Renewable Energy Laboratory (NREL) started a project in 2000 to record long-term, high-frequency (1-Hz) wind power output data from large commercial wind power plants. Outputs from about 330 MW of wind generating capacity from wind power plants in Buffalo Ridge, Minnesota, and Storm Lake, Iowa, are being recorded. Analysis of the collected data shows that although very short-term wind power fluctuations are stochastic, the persistent nature of wind and the large number of turbines in a wind power plant tend to limit the magnitudes and rates of changes in the levels of wind power. Analyses of power data confirm that spatial separation greatly reduces variations in the combined wind power output relative to output from a single wind power plant. Data show that high frequency variations of wind power from two wind power plants 200 km apart are independent of each other, but low frequency power changes can be highly correlated. This fact suggests that time-synchronized power data and meteorological data can aid in the development of statistical models for wind power forecasting.

scholarly journals Economic feasibility of developing large scale solar photovoltaic power plants in Spain

2019 ◽  
Vol 122 ◽  
pp. 02004 ◽  
Author(s):  
Javier Menéndez ◽  
Jorge Loredo
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Large Scale ◽  
Economic Feasibility ◽  
Solar Irradiation ◽  
Solar Photovoltaic ◽  
Solar Pv ◽  
Photovoltaic Energy ◽  
Profitability Analysis ◽  
Photovoltaic Power

In 2017, electricity generation from renewable sources contributed more than one quarter (30.7%) to total EU-28 gross electricity consumption. Wind power is for the first time the most important source, followed closely by hydro power. The growth in electricity from photovoltaic energy has been dramatic, rising from just 3.8 TWh in 2007, reaching a level of 119.5 TWh in 2017. Over this period, the contribution of photovoltaic energy to all electricity generated in the EU-28 from renewable energy sources increased from 0.7% to 12.3%. During this period the investment cost of a photovoltaic power plant has decreased considerably. Fundamentally, the cost of solar panels and inverters has decreased by more than 50%. The solar photovoltaic energy potential depends on two parameters: global solar irradiation and photovoltaic panel efficiency. The average solar irradiation in Spain is 1,600 kWh m-2. This paper analyzes the economic feasibility of developing large scale solar photovoltaic power plants in Spain. Equivalent hours between 800-1,800 h year-1 and output power between 100-400 MW have been considered. The profitability analysis has been carried out considering different prices of the electricity produced in the daily market (50-60 € MWh-1). Net Present Value (NPV) and Internal Rate of Return (IRR) were estimated for all scenarios analyzed. A solar PV power plant with 400 MW of power and 1,800 h year-1, reaches a NPV of 196 M€ and the IRR is 11.01%.

scholarly journals Dynamic wavelet correlation analysis for multivariate climate time series

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Josué M. Polanco-Martínez ◽  
Javier Fernández-Macho ◽  
Martín Medina-Elizalde
Keyword(s):  
Time Series ◽  
Tropical Cyclone ◽  
North Atlantic ◽  
Large Scale ◽  
Southern Oscillation ◽  
Atlantic Multidecadal Oscillation ◽  
Last Millennium ◽  
Climate Data ◽  
The North ◽  
Climate Time Series

AbstractThe wavelet local multiple correlation (WLMC) is introduced for the first time in the study of climate dynamics inferred from multivariate climate time series. To exemplify the use of WLMC with real climate data, we analyse Last Millennium (LM) relationships among several large-scale reconstructed climate variables characterizing North Atlantic: i.e. sea surface temperatures (SST) from the tropical cyclone main developmental region (MDR), the El Niño-Southern Oscillation (ENSO), the North Atlantic Multidecadal Oscillation (AMO), and tropical cyclone counts (TC). We examine the former three large-scale variables because they are known to influence North Atlantic tropical cyclone activity and because their underlying drivers are still under investigation. WLMC results obtained for these multivariate climate time series suggest that: (1) MDRSST and AMO show the highest correlation with each other and with respect to the TC record over the last millennium, and: (2) MDRSST is the dominant climate variable that explains TC temporal variability. WLMC results confirm that this method is able to capture the most fundamental information contained in multivariate climate time series and is suitable to investigate correlation among climate time series in a multivariate context.

Discussion on Asset-Backed Securitization of PV Power Plants

2014 ◽  
Vol 960-961 ◽  
pp. 1536-1541
Author(s):  
Yi Feng Wang
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Financial Institution ◽  
Plant Construction ◽  
Financing Channels ◽  
Substantial Investment ◽  
Long Time ◽  
Asset Backed Securities ◽  
Financing Problem ◽  
Capital Intensive

At present, it universally exists that the financing problem confronts photovoltaic (PV) power plant construction in our country. The PV power plant construction in industry falls into the capital-intensive enterprises with the feature of long-time development, substantial investment needed but relatively fixed income. Thereby, it is greatly appropriate for solar securitization to address the financing needs of the industry with its characteristic and realize diversified financing channels. In this paper, firstly, the feasibility and necessity of ABS financing for PV industry is discussed. Then, we elaborate the current four construction modes of PV power plant, namely, transfer of beneficial interest of power charge, credit increment of financing platform company, BOT (build-operate-transfer), and financing leasing, according to which the financial institution designs the asset-backed securities supported by the beneficial interest of the power plant. Further, we analyze the main problems and challenges of carrying out the ABS business. Countermeasures and suggestion ns are put forward finally.

Study on Project of Photovoltaic Power Plant Connecting to the Grid

2013 ◽  
Vol 380-384 ◽  
pp. 3111-3114
Author(s):  
Yi Shi Shu ◽  
Li Li Ma ◽  
Chao Peng
Keyword(s):  
Power Plant ◽  
Power Plants ◽  
Large Scale ◽  
Pv System ◽  
Photovoltaic Generation ◽  
Large Capacity ◽  
Photovoltaic Power

Large scale photovoltaic generation is another way to generate electricity.When a large capacity PV system connected to the grid,much impact could be brought to the grid due to its uncertainty. In this paper, there are research and analysis about the technology and characteristics of the photovoltaic power plants connected to the grid, make a strong practical impacts.

Grid-Connected Nuclear Power Plant Key Issues Summary

2014 ◽  
Vol 521 ◽  
pp. 530-535
Author(s):  
Meng Wang ◽  
Jian Ding ◽  
Tian Tang ◽  
Zhang Sui Lin ◽  
Zhen Da Hu ◽  
...  
Keyword(s):  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Large Scale ◽  
Power Modeling ◽  
Modeling Simulation ◽  
Load Following ◽  
Key Issues ◽  
The Impact

The current situation of nuclear power plants at home and abroad is described, and the impact of large-scale nuclear power accessing to the grid is analyzed, specifically in the aspects of nuclear power modeling, simulation, load following, reliability, fault diagnosis, etc. Nuclear power accessing to the grid will bring a series of problems, the causes of each problem, the main solutions and future development directions are summarized.

Output Power Correlation Between Adjacent Wind Power Plants*

2003 ◽  
Vol 125 (4) ◽  
pp. 551-555 ◽  
Author(s):  
Yih-huei Wan ◽  
Michael Milligan ◽  
Brian Parsons
Keyword(s):  
Power Plant ◽  
Wind Power ◽  
High Frequency ◽  
Power Plants ◽  
Meteorological Data ◽  
Low Frequency ◽  
Spatial Separation ◽  
Rate Of Change ◽  
Wind Power Plant ◽  
Wind Power Plants

The National Renewable Energy Laboratory (NREL) started a project in 2000 to record long-term, high-frequency (1-Hz) wind power data from large commercial wind power plants in the Midwestern United States. Outputs from about 330 MW of installed wind generating capacity from wind power plants in Lake Benton, MN, and Storm Lake, Iowa, are being recorded. Analysis of the collected data shows that although very short-term wind power fluctuations are stochastic, the persistent nature of wind and the large number of turbines in a wind power plant tend to limit the magnitude of fluctuations and rate of change in wind power production. Analyses of power data confirms that spatial separation of turbines greatly reduces variations in their combined wind power output when compared to the output of a single wind power plant. Data show that high-frequency variations of wind power from two wind power plants 200 km apart are independent of each other, but low-frequency power changes can be highly correlated. This fact suggests that time-synchronized power data and meteorological data can aid in the development of statistical models for wind power forecasting.

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